Ocimum kilimandscharicum 4CL11 negatively regulates adventitious root development via accumulation of flavonoid glycosides.

4-Coumarate-CoA Ligase (4CL) is an important enzyme in the phenylpropanoid biosynthesis pathway. Multiple 4CLs are identified in Ocimum species; however, their in planta functions remain enigmatic. In this study, we independently overexpressed three Ok4CL isoforms from Ocimum kilimandscharicum (Ok4CL7, -11, and -15) in Nicotiana benthamiana. Interestingly, Ok4CL11 overexpression (OE) caused a rootless or reduced root growth phenotype, whereas overexpression of Ok4CL15 produced normal adventitious root (AR) growth. Ok4CL11 overexpression in N. benthamiana resulted in upregulation of genes involved in flavonoid biosynthesis and associated glycosyltransferases accompanied by accumulation of specific flavonoid-glycosides (kaempferol-3-rhamnoside, kaempferol-3,7-O-bis-alpha-l-rhamnoside [K3,7R], and quercetin-3-O-rutinoside) that possibly reduced auxin levels in plants, and such effects were not seen for Ok4CL7 and -15. Docking analysis suggested that auxin transporters (PINs/LAXs) have higher binding affinity to these specific flavonoid-glycosides, and thus could disrupt auxin transport/signaling, which cumulatively resulted in a rootless phenotype. Reduced auxin levels, increased K3,7R in the middle and basal stem sections, and grafting experiments (intra and inter-species) indicated a disruption of auxin transport by K3,7R and its negative effect on AR development. Supplementation of flavonoids and the specific glycosides accumulated by Ok4CL11-OE to the wild-type N. benthamiana explants delayed the AR emergence and also inhibited AR growth. While overexpression of all three Ok4CLs increased lignin accumulation, flavonoids, and their specific glycosides were accumulated only in Ok4CL11-OE lines. In summary, our study reveals unique indirect function of Ok4CL11 to increase specific flavonoids and their glycosides, which are negative regulators of root growth, likely involved in inhibition of auxin transport and signaling.

Three-dimensional integral imaging-based image descattering and recovery using physics informed unsupervised CycleGAN.

Image restoration and denoising has been a challenging problem in optics and computer vision. There has been active research in the optics and imaging communities to develop a robust, data-efficient system for image restoration tasks. Recently, physics-informed deep learning has received wide interest in scientific problems. In this paper, we introduce a three-dimensional integral imaging-based physics-informed unsupervised CycleGAN algorithm for underwater image descattering and recovery using physics-informed CycleGAN (Generative Adversarial Network). The system consists of a forward and backward pass. The base architecture consists of an encoder and a decoder. The encoder takes the clean image along with the depth map and the degradation parameters to produce the degraded image. The decoder takes the degraded image generated by the encoder along with the depth map and produces the clean image along with the degradation parameters. In order to provide physical significance for the input degradation parameter w.r.t a physical model for the degradation, we also incorporated the physical model into the loss function. The proposed model has been assessed under the dataset curated through underwater experiments at various levels of turbidity. In addition to recovering the original image from the degraded image, the proposed algorithm also helps to model the distribution under which the degraded images have been sampled. Furthermore, the proposed three-dimensional Integral Imaging approach is compared with the traditional deep learning-based approach and 2D imaging approach under turbid and partially occluded environments. The results suggest the proposed approach is promising, especially under the above experimental conditions.

Underwater object detection and temporal signal detection in turbid water using 3D-integral imaging and deep learning.

Underwater scattering caused by suspended particles in the water severely degrades signal detection performance and poses significant challenges to the problem of object detection. This paper introduces an integrated dual-function deep learning-based underwater object detection and classification and temporal signal detection algorithm using three-dimensional (3D) integral imaging (InIm) under degraded conditions. The proposed system is an efficient object classification and temporal signal detection system for degraded environments such as turbidity and partial occlusion and also provides the object range in the scene. A camera array captures the underwater objects in the scene and the temporally encoded binary signals transmitted for the purpose of communication. The network is trained using a clear underwater scene without occlusion, whereas test data is collected in turbid water with partial occlusion. Reconstructed 3D data is the input to a You Look Only Once (YOLOv4) neural network for object detection and a convolutional neural network-based bidirectional long short-term memory network (CNN-BiLSTM) is used for temporal optical signal detection. Finally, the transmitted signal is decoded. In our experiments, 3D InIm provides better image reconstruction in a degraded environment over 2D sensing-based methods. Also, reconstructed 3D images segment out the object of interest from occlusions and background which improves the detection accuracy of the network with 3D InIm. To the best of our knowledge, this is the first report that combines deep learning with 3D InIm for simultaneous and integrated underwater object detection and optical signal detection in degraded environments.

Tyramine-Mediated Hyperactivity Modulates the Dietary Habits in Helicoverpa armigera.

Helicoverpa armigera exhibits extensive variability in feeding habits and food selection. Neuronal regulation of H. armigera feeding behavior is primarily influenced by biogenic amines such as Tyramine (TA) and Octopamine (OA). The molecular responses of H. armigera to dietary challenges in the presence of TA or OA have yet to be studied. This investigation dissects the impact of OA and TA on H. armigera feeding choices and behaviors under non-host nutritional stress. It has been observed that feeding behavior remains unaltered during the exogenous administration of OA and TA through an artificial diet (AD). Ingestion of higher OA or TA concentrations leads to increased mortality. OA and TA treatment in combination with host and non-host diets results in the induction of feeding and higher locomotion toward food, particularly in the case of TA treatment. Increased expression of markers, prominin-like, and tachykinin-related peptide receptor-like transcripts further assessed increased locomotion activity. Insects subjected to a non-host diet with TA treatment exhibited increased feeding and overexpression of the feeding indicator, the Neuropeptide F receptor, and the feeding regulator, Sulfakinin, compared with other conditions. Expression of sensation and biogenic amine synthesis genesis elevated in insects fed a non-host diet in combination with OA or TA. Metabolomics analysis revealed a decreased concentration of the feeding behavior elicitor, dopamine, in insects fed a non-host diet containing TA. This work highlights the complex interplay between biogenic amine functions during dietary stress and suggests the role of tyramine in feeding promotion under stressed conditions.

Regulation of trehalose metabolism in insects: from genes to the metabolite window.

Trehalose is a major circulatory sugar in the haemolymph of insects. It provides instant energy and protection against stress. Trehalose metabolism is associated with insect growth and development. The architecture and spatio-temporal expression dynamics of trehalose metabolism and transport genes are key for regulation. These genes are controlled by various transcription factors, largely linked to nutrition, insect development, and metamorphosis. Also, trehalose levels are affected by substrate affinities and modifications of enzymes involved in the pathway. A feedback mechanism involving the precursors and products can regulate trehalose metabolism. Further, the neuroendocrine system controls trehalose levels under normal and stressed conditions by producing different hormones. Hypotrehalosemic hormones work under surplus energy conditions to activate haemolymph trehalose uptake and degradation. In contrast, hypertrehalosemic hormones stimulate trehalose production in the fat body and its transport to the haemolymph. However, trehalose metabolism regulation in insects needs to be studied in detail. This review discusses aspects of trehalose synthesis, transport, and degradation dynamics in developmental transition and stress response. Unraveling the epigenetic factors, transcriptional control and chemical or genetic modulators can provide further insights into the intricate regulation of trehalose in a development- and tissue-specific manner. This molecular information about effectors and regulators of trehalose metabolism can be applied in developing diverse biotechnological applications.

End-to-end integrated pipeline for underwater optical signal detection using 1D integral imaging capture with a convolutional neural network.

Underwater optical signal detection performance suffers from occlusion and turbidity in degraded environments. To tackle these challenges, three-dimensional (3D) integral imaging (InIm) with 4D correlation-based and deep-learning-based signal detection approaches have been proposed previously. Integral imaging is a 3D technique that utilizes multiple cameras to capture multiple perspectives of the scene and uses dedicated algorithms to reconstruct 3D images. However, these systems may require high computational requirements, multiple separate preprocessing steps, and the necessity for 3D image reconstruction and depth estimation of the illuminating modulated light source. In this paper, we propose an end-to-end integrated signal detection pipeline that uses the principle of one-dimensional (1D) InIm to capture angular and intensity of ray information but without the computational burden of full 3D reconstruction and depth estimation of the light source. The system is implemented with a 1D camera array instead of 2D camera array and is trained with a convolutional neural network (CNN). The proposed approach addresses many of the aforementioned shortcomings to improve underwater optical signal detection speed and performance. In our experiment, the temporal-encoded signals are transmitted by a light-emitting diode passing through a turbid and partial occluded environment which are captured by a 1D camera array. Captured video frames containing the spatiotemporal information of the optical signals are then fed into the CNN for signal detection without the need for depth estimation and 3D scene reconstruction. Thus, the entire processing steps are integrated and optimized by deep learning. We compare the proposed approach with the previously reported depth estimated 3D InIm with 3D scene reconstruction and deep learning in terms of computational cost at receiver's end and detection performance. Moreover, a comparison with conventional 2D imaging is also included. The experimental results show that the proposed approach performs well in terms of detection performance and computational cost. To the best of our knowledge, this is the first report on signal detection in degraded environments with computationally efficient end-to-end integrated 1D InIm capture stage with integrated deep learning for classification.

Mass Transport via In-Plane Nanopores in Graphene Oxide Membranes.

Angstrom-confined solvents in 2D laminates can travel through interlayer spacings, through gaps between adjacent sheets, and via in-plane pores. Among these, experimental access to investigate the mass transport through in-plane pores is lacking. Our experiments allow an understanding of this mass transport via the controlled variation of oxygen functionalities, size and density of in-plane pores in graphene oxide membranes. Contrary to expectations, our transport experiments show that higher in-plane pore densities may not necessarily lead to higher water permeability. We observed that membranes with a high in-plane pore density but a low amount of oxygen functionalities exhibit a complete blockage of water. However, when water-ethanol mixtures with a weaker hydrogen network are used, these membranes show an enhanced permeation. Our combined experimental and computational results suggest that the transport mechanism is governed by the attraction of the solvents toward the pores with functional groups and hindered by the strong hydrogen network of water formed under angstrom confinement.

Functional Diversity of the Lepidopteran ATP-Binding Cassette Transporters.

The ATP-binding cassette (ABC) transporter gene family is ubiquitous in the living world. ABC proteins bind and hydrolyze ATP to transport a myriad of molecules across various lipid-containing membrane systems. They have been studied well in plants for transport of a variety of compounds and particularly, in vertebrates due to their direct involvement in resistance mechanisms against several toxic molecules/metabolites. ABC transporters in insects are found within large multigene families involved in the efflux of chemical insecticides and toxic/undesired metabolites originating from food and endogenous metabolism. This review deals with ABC transporter subfamilies of few agronomically important Lepidopteran pests. The transcriptional dynamics and regulation of ABC transporters during insect development emphasizes their functional diversity against insecticides, Cry toxins, and plant specialized metabolites. To generate insights about molecular function and physiological roles of ABCs, functional and structural characterization is necessary. Also, expansion and divergence of ABC transporter gene subfamilies in Lepidopteran insects needs more systematic investigation. We anticipate that newer methods of insect control in agriculture can benefit from an understanding of ABC transporter interactions with a vast range of natural specialized molecules and synthetic compounds.

Recent trends in covalent functionalization of 2D materials.

Covalent functionalization of the surface is more crucial in 2D materials than in conventional bulk materials because of their atomic thinness, large surface-to-volume ratio, and uniform surface chemical potential. Because 2D materials are composed of two surfaces with no dangling bond, covalent functionalization enables us to improve or precisely modify the electrical, mechanical, and chemical properties. In this review, we summarize the covalent functionalization methods and related changes in properties. First, we discuss possible sites for functionalization. Consequently, functionalization techniques are introduced, followed by the direct synthesis of functionalized 2D materials and characterization methods of functionalized 2D materials. Finally, we suggest how the issues may be solved to enlarge the research area and understanding of the chemistry of 2D materials. This review will help in understanding the functionalization of 2D materials.

Functional groups in graphene oxide.

Graphene oxide has aroused significant interest for a range of applications owing to their outstanding physico-chemical properties. Specifically, the presence of a large number of reactive chemical moieties such as hydroxyl, carboxyl, epoxide, and sp2 carbon allows these novel materials to be tailored with additional functionalities with the purpose of tuning intrinsic properties. There has been a vivid discussion on the non-covalent modification of GO; however, a comprehensive summary of the chemical functionalization which enables forming a stable particle is still elusive. Hence, in this study, we summarize recently advanced methodologies used for designing the functional GO for their use in specific applications. Together with a brief discussion on the essential characterization techniques, this study will provide fundamental insight into the latest developments in the preparation of covalently modified GO derivatives, thereby leading to their broader utilization in future.

Challenges and Changes in Pediatric Surgical Practice during the COVID-19 Pandemic Era.

Aim: Working practices in pediatric surgery underwent enormous changes during the era of the COVID-19 pandemic. While certain surgical conditions in children can be managed temporarily with nonsurgical options, most neonates with congenital surgical malformations require emergent operations. We discuss the challenges faced; measures adopted in dealing with surgical emergencies and analyze the diagnoses and outcomes of patients with COVID-19 infection in our institute during the pandemic era. Materials and Methods: When the lockdown was imposed, it was mandated that all elective procedures should be put on hold. We formulated criteria for triaging procedures as emergent, urgent, and elective. A standard operating protocol was devised regarding admission, pre and postoperative management. Protocols for surgical procedures were established in a separate Covid-designated operation room including a specified sequence of donning and doffing personal protective equipment. Results: In the COVID era, from March 23, 2020 to mid-July 2021, 1282 surgeries have been done in our department, 344 emergencies and 461 planned procedures, which include 31 COVID-19 positive cases, with overall good outcomes. 103 emergency surgeries were done during the first wave (March-end to June 2020), and 103during the second wave (April to mid-June 2021). Moreover, not a single healthcare worker in the department has been infected. Conclusion: Pediatric Surgeons are adapting to the new guidelines to continue to provide emergency services with safe and effective care to their patients during the COVID-19 pandemic. Simultaneously, focus on personal and staff protection is ensured to keep the healthcare workers healthy and able to discharge their duties adequately.

DLC1 SAM domain-binding peptides inhibit cancer cell growth and migration by inactivating RhoA.

Deleted-in-liver cancer 1 (DLC1) exerts its tumor suppressive function mainly through the Rho-GTPase-activating protein (RhoGAP) domain. When activated, the domain promotes the hydrolysis of RhoA-GTP, leading to reduced cell migration. DLC1 is kept in an inactive state by an intramolecular interaction between its RhoGAP domain and the DLC1 sterile α motif (SAM) domain. We have shown previously that this autoinhibited state of DLC1 may be alleviated by tensin-3 (TNS3) or PTEN. We show here that the TNS3/PTEN-DLC1 interactions are mediated by the C2 domains of the former and the SAM domain of the latter. Intriguingly, the DLC1 SAM domain was capable of binding to specific peptide motifs within the C2 domains. Indeed, peptides containing the binding motifs were highly effective in blocking the C2-SAM domain-domain interaction. Importantly, when fused to the tat protein-transduction sequence and subsequently introduced into cells, the C2 peptides potently promoted the RhoGAP function in DLC1, leading to decreased RhoA activation and reduced tumor cell growth in soft agar and migration in response to growth factor stimulation. To facilitate the development of the C2 peptides as potential therapeutic agents, we created a cyclic version of the TNS3 C2 domain-derived peptide and showed that this peptide readily entered the MDA-MB-231 breast cancer cells and effectively inhibited their migration. Our work shows, for the first time, that the SAM domain is a peptide-binding module and establishes the framework on which to explore DLC1 SAM domain-binding peptides as potential therapeutic agents for cancer treatment.

Molecular Characterization of the ß2-like Octopamine Receptor of Helicoverpa armigera.

Helicoverpa armigera is a devastating polyphagous and cosmopolitan crop pest. There are reports of this insect being resistant to a variety of pesticides raising concern worldwide. The Octopamine (OA) binding ß2-like receptor (OAR), a GPCR, is widely distributed in the nervous system of the insect and plays essential roles in the physiology and development and thus is an important target for insecticides. Yet, the molecular characterization of the H. armigera OAR (HarmOAR) and rational design of compounds based on this receptor is lacking. As a first step, we performed multiple sequence alignment of all insect OARs, which revealed that the sequences contained all conserved class A GPCR motifs. Phylogenetic studies showed clade-specific variations in the protein sequences primarily arising owing to differences in the ICL3 loop region. Further, a structural model of HarmOAR was built using the inactive human ß2AR as a template. 0.9 µs atomistic simulations revealed conserved inter helical contacts and water molecules of HarmOAR. The detailed binding of octopamine was studied using molecular docking and 0.3 µs atomistic simulations. Twenty-two insecticides active against octopamine receptors of other insects were compiled and docked to HarmOAR followed by rescoring with binding free energies to prioritize them for H. armigera. Our study suggests α-terpineol to be a good candidate as an insecticide or insect repellent for Helicoverpa armigera.

Spatio-temporal continuous gesture recognition under degraded environments: performance comparison between 3D integral imaging (InIm) and RGB-D sensors.

In this paper, we introduce a deep learning-based spatio-temporal continuous human gesture recognition algorithm under degraded conditions using three-dimensional (3D) integral imaging. The proposed system is shown as an efficient continuous human gesture recognition system for degraded environments such as partial occlusion. In addition, we compare the performance between the 3D integral imaging-based sensing and RGB-D sensing for continuous gesture recognition under degraded environments. Captured 3D data serves as the input to a You Look Only Once (YOLOv2) neural network for hand detection. Then, a temporal segmentation algorithm is employed to segment the individual gestures from a continuous video sequence. Following segmentation, the output is fed to a convolutional neural network-based bidirectional long short-term memory network (CNN-BiLSTM) for gesture classification. Our experimental results suggest that the proposed deep learning-based spatio-temporal continuous human gesture recognition provides substantial improvement over both RGB-D sensing and conventional 2D imaging system. To the best of our knowledge, this is the first report of 3D integral imaging-based continuous human gesture recognition with deep learning and the first comparison between 3D integral imaging and RGB-D sensors for this task.

Optical signal detection in turbid water using multidimensional integral imaging with deep learning.

Optical signal detection in turbid and occluded environments is a challenging task due to the light scattering and beam attenuation inside the medium. Three-dimensional (3D) integral imaging is an imaging approach which integrates two-dimensional images from multiple perspectives and has proved to be useful for challenging conditions such as occlusion and turbidity. In this manuscript, we present an approach for the detection of optical signals in turbid water and occluded environments using multidimensional integral imaging employing temporal encoding with deep learning. In our experiments, an optical signal is temporally encoded with gold code and transmitted through turbid water via a light-emitting diode (LED). A camera array captures videos of the optical signals from multiple perspectives and performs the 3D signal reconstruction of temporal signal. The convolutional neural network-based bidirectional Long Short-Term Network (CNN-BiLSTM) network is trained with clear water video sequences to perform classification on the binary transmitted signal. The testing data was collected in turbid water scenes with partial signal occlusion, and a sliding window with CNN-BiLSTM-based classification was performed on the reconstructed 3D video data to detect the encoded binary data sequence. The proposed approach is compared to previously presented correlation-based detection models. Furthermore, we compare 3D integral imaging to conventional two-dimensional (2D) imaging for signal detection using the proposed deep learning strategy. The experimental results using the proposed approach show that the multidimensional integral imaging-based methodology significantly outperforms the previously reported approaches and conventional 2D sensing-based methods. To the best of our knowledge, this is the first report on underwater signal detection using multidimensional integral imaging with deep neural networks.

Human gesture recognition under degraded environments using 3D-integral imaging and deep learning.

In this paper, we propose a spatio-temporal human gesture recognition algorithm under degraded conditions using three-dimensional integral imaging and deep learning. The proposed algorithm leverages the advantages of integral imaging with deep learning to provide an efficient human gesture recognition system under degraded environments such as occlusion and low illumination conditions. The 3D data captured using integral imaging serves as the input to a convolutional neural network (CNN). The spatial features extracted by the convolutional and pooling layers of the neural network are fed into a bi-directional long short-term memory (BiLSTM) network. The BiLSTM network is designed to capture the temporal variation in the input data. We have compared the proposed approach with conventional 2D imaging and with the previously reported approaches using spatio-temporal interest points with support vector machines (STIP-SVMs) and distortion invariant non-linear correlation-based filters. Our experimental results suggest that the proposed approach is promising, especially in degraded environments. Using the proposed approach, we find a substantial improvement over previously published methods and find 3D integral imaging to provide superior performance over the conventional 2D imaging system. To the best of our knowledge, this is the first report that examines deep learning algorithms based on 3D integral imaging for human activity recognition in degraded environments.

Signal detection in turbid water using temporally encoded polarimetric integral imaging.

To improve signal detection in a turbid medium, we propose temporally encoded single shot polarimetric integral imaging. An optical signal is temporally encoded using gold coded sequences and transmitted through a turbid medium. The encoded signals are captured as a sequence of elemental images by two orthogonal polarized image sensor arrays. Polarimetric and polarization difference imaging are used to suppress the partially polarized and unpolarized background noise such that only the polarized ballistic signal photons are captured at the sensor. Multidimensional integral imaging is used to obtain 4D reconstructed data, and multidimensional nonlinear correlation is performed on the reconstructed data to detect the optical signal. We compare the effectiveness of the proposed polarimetric underwater optical signal detection approach to conventional (non-polarimetric) integral imaging-based and 2D imaging-based signal detection systems. The underwater signal detection capabilities are measured through performance metrics such as receiver operating characteristic (ROC) curves, the area under the curve (AUC), and the number of detection errors. Furthermore, statistical measures, including the Kullback-Leibler divergence, signal-to-noise ratio (SNR), and peak-to-correlation energy (PCE), are also calculated to show the improved performance of the proposed system. Our experimental results show that the proposed polarimetric integral-imaging approach significantly outperforms the conventional imaging-based methods. To the best of our knowledge, this is the first report on temporally encoded single shot polarimetric integral imaging for signal detection in turbid water.

Optical 4D signal detection in turbid water by multi-dimensional integral imaging using spatially distributed and temporally encoded multiple light sources.

We propose an underwater optical signal detection system based on multi-dimensional integral imaging with spatially distributed multiple light sources and four-dimensional (4D) spatial-temporal correlation. We demonstrate our system for the detection of optical signals in turbid water. A 4D optical signal is generated from a three-dimensional (3D) spatial distribution of underwater light sources, which are temporally encoded using spread spectrum techniques. The optical signals are captured by an array of cameras, and 3D integral imaging reconstruction is performed, followed by multi-dimensional correlation to detect the optical signal. Inclusion of multiple light sources located at different depths allows for successful signal detection at turbidity levels not feasible using only a single light source. We consider the proposed system under varied turbidity levels using both Pseudorandom and Gold Codes for temporal signal coding. We also compare the effectiveness of the proposed underwater optical signal detection system to a similar system using only a single light source and compare between conventional and integral imaging-based signal detection. The underwater signal detection capabilities are measured through performance-based metrics such as receiver operating characteristic (ROC) curves, the area under the curve (AUC), and the number of detection errors. Furthermore, statistical analysis, including Kullback-Leibler divergence and Bhattacharya distance, shows improved performance of the proposed multi-source integral imaging underwater system. The proposed integral-imaging based approach is shown to significantly outperform conventional imaging-based methods.

Efficient Multi-Object Detection and Smart Navigation Using Artificial Intelligence for Visually Impaired People.

Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize different objects, and auditory inputs are provided to the user in real time, which gives better understanding to the visually impaired person about their surroundings. A deep-learning model is trained with multiple images of objects that are highly relevant to the visually impaired person. Training images are augmented and manually annotated to bring more robustness to the trained model. In addition to computer vision-based techniques for object recognition, a distance-measuring sensor is integrated to make the device more comprehensive by recognizing obstacles while navigating from one place to another. The auditory information that is conveyed to the user after scene segmentation and obstacle identification is optimized to obtain more information in less time for faster processing of video frames. The average accuracy of this proposed method is 95.19% and 99.69% for object detection and recognition, respectively. The time complexity is low, allowing a user to perceive the surrounding scene in real time.

Semirational design and engineering of grapevine glucosyltransferases for enhanced activity and modified product selectivity.

Uridine diphosphate-dependent glycosyltransferases (UGTs) catalyze the transfer of a diversity of sugars to several acceptor molecules and often exhibit distinct substrate specificity. Modulation of glycosyltransferases for increased catalytic activity and altered substrate or product specificity are the key manipulations for the biotechnological use of glycosyltransferases in various biosynthetic processes. Here, we have engineered the binding pocket of three previously characterized Vitis vinifera glycosyltransferases, UGT88F12, UGT72B27 and UGT92G6, by structure-guided in silico mutagenesis to facilitate the interactions of active site residues with flavonol glucosides and thus modify substrate specificity and activity. Site-directed mutagenesis at selected sites, followed with liquid chromatography-mass spectrometry based activity assays, exhibited that mutant UGTs were altered in product selectivity and activity as compared to the wild-type enzymes. Mutant UGTs produced larger amounts of flavonol di-monosaccharide glucosides, which imply that the mutations led to structural changes that increased the volume of the binding pocket to accommodate a larger substrate and to release larger products at ease. Mutants showed increased activity and modified product specificity. Thus, structure-based systematic mutations of the amino acid residues in the binding pocket can be explored for the generation of engineered UGTs for diverse biotechnological applications.